diff --git a/Lib/optimize/tests/test_optimize.py b/Lib/optimize/tests/test_optimize.py
new file mode 100644
index 000000000000..f39f29d739e1
--- /dev/null
+++ b/Lib/optimize/tests/test_optimize.py
@@ -0,0 +1,124 @@
+""" Unit tests for optimization routines
+Author: Ed Schofield
+Nov 2005
+"""
+
+from scipy.test.testing import *
+
+set_package_path()
+from scipy import *
+from scipy.linalg import norm
+# from scipy import optimize, array, zeros, float64
+restore_path()
+
+
+class test_optimize(ScipyTestCase):
+    """ Test case for a simple constrained entropy maximization problem
+    (the machine translation example of Berger et al in
+    Computational Linguistics, vol 22, num 1, pp 39--72, 1996.)
+    """
+    def setUp(self):
+        self.F = array([[1,1,1],[1,1,0],[1,0,1],[1,0,0],[1,0,0]])
+        self.K = array([1., 0.3, 0.5])
+        self.startparams = zeros(3, float64)
+        self.solution = array([0., -0.524869316, 0.487525860])
+        self.maxiter = 1000
+    
+
+    def func(self, x):
+        log_pdot = dot(self.F, x)
+        logZ = log(sum(exp(log_pdot)))
+        f = logZ - dot(self.K, x)
+        return f
+    
+
+    def grad(self, x):
+        log_pdot = dot(self.F, x)
+        logZ = log(sum(exp(log_pdot)))
+        p = exp(log_pdot - logZ)
+        return dot(self.F.transpose(), p) - self.K
+    
+
+    def check_cg(self):
+        """ conjugate gradient optimization routine
+        """
+        retval = optimize.fmin_cg(self.func, self.startparams, self.grad, (), \
+                                  maxiter=self.maxiter, \
+                                  full_output=True, disp=False, retall=False)
+                
+        (params, fopt, func_calls, grad_calls, warnflag) = retval
+
+        err = abs(self.func(params) - self.func(self.solution))
+        #print "CG: Difference is: " + str(err)
+        assert err < 1e-6
+
+
+    def check_bfgs(self):
+        """ Broyden-Fletcher-Goldfarb-Shanno optimization routine
+        """
+        retval = optimize.fmin_bfgs(self.func, self.startparams, self.grad, \
+                                    args=(), maxiter=self.maxiter, \
+                                    full_output=True, disp=False, retall=False)
+                
+        (params, fopt, gopt, Hopt, func_calls, grad_calls, warnflag) = retval
+
+        err = abs(self.func(params) - self.func(self.solution))
+        #print "BFGS: Difference is: " + str(err)
+        assert err < 1e-6
+
+
+    def check_powell(self):
+        """ Powell (direction set) optimization routine
+        """
+        retval = optimize.fmin_powell(self.func, self.startparams, \
+                                    args=(), maxiter=self.maxiter, \
+                                    full_output=True, disp=False, retall=False)
+                
+        (params, fopt, direc, numiter, func_calls, warnflag) = retval
+
+        err = abs(self.func(params) - self.func(self.solution))
+        #print "Powell: Difference is: " + str(err)
+        assert err < 1e-6
+
+    def check_neldermead(self):
+        """ Nelder-Mead simplex algorithm
+        """
+        retval = optimize.fmin(self.func, self.startparams, \
+                                    args=(), maxiter=self.maxiter, \
+                                    full_output=True, disp=False, retall=False)
+                
+        (params, fopt, numiter, func_calls, warnflag) = retval
+
+        err = abs(self.func(params) - self.func(self.solution))
+        #print "Nelder-Mead: Difference is: " + str(err)
+        assert err < 1e-6
+
+    def check_ncg(self):
+        """ line-search Newton conjugate gradient optimization routine
+        """
+        retval = optimize.fmin_ncg(self.func, self.startparams, self.grad,\
+                                    args=(), maxiter=self.maxiter, \
+                                    full_output=False, disp=False, retall=False)
+                
+        params = retval
+
+        err = abs(self.func(params) - self.func(self.solution))
+        #print "NCG: Difference is: " + str(err)
+        assert err < 1e-6
+
+
+    def check_l_bfgs_b(self):
+        """ limited-memory bound-constrained BFGS algorithm
+        """
+        retval = optimize.fmin_l_bfgs_b(self.func, self.startparams, self.grad,\
+                                    args=(), maxfun=self.maxiter)
+                
+        (params, fopt, d) = retval
+
+        err = abs(self.func(params) - self.func(self.solution))
+        #print "LBFGSB: Difference is: " + str(err)
+        assert err < 1e-6
+
+
+if __name__ == "__main__":
+    ScipyTest().run()